Overview

Dataset statistics

Number of variables12
Number of observations756
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory76.8 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

oil_brent is highly overall correlated with oil_dubai and 8 other fieldsHigh correlation
oil_dubai is highly overall correlated with oil_brent and 8 other fieldsHigh correlation
coffee_arabica is highly overall correlated with oil_brent and 8 other fieldsHigh correlation
coffee_robustas is highly overall correlated with coffee_arabica and 2 other fieldsHigh correlation
tea_columbo is highly overall correlated with oil_brent and 7 other fieldsHigh correlation
tea_kolkata is highly overall correlated with oil_brent and 8 other fieldsHigh correlation
tea_mombasa is highly overall correlated with oil_brent and 8 other fieldsHigh correlation
sugar_eu is highly overall correlated with oil_brent and 2 other fieldsHigh correlation
sugar_us is highly overall correlated with oil_brent and 7 other fieldsHigh correlation
sugar_world is highly overall correlated with oil_brent and 7 other fieldsHigh correlation
Year is highly overall correlated with oil_brent and 8 other fieldsHigh correlation

Reproduction

Analysis started2023-12-07 23:01:20.182776
Analysis finished2023-12-07 23:02:17.540820
Duration57.36 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

oil_brent
Real number (ℝ)

Distinct528
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.724944
Minimum1.21
Maximum133.87304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2023-12-08T00:02:17.871614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.21
5-th percentile1.33
Q110.564999
median20.48913
Q347.1575
95-th percentile108.25964
Maximum133.87304
Range132.66304
Interquartile range (IQR)36.592501

Descriptive statistics

Standard deviation31.885368
Coefficient of variation (CV)0.97434447
Kurtosis0.59426373
Mean32.724944
Median Absolute Deviation (MAD)18.40913
Skewness1.203709
Sum24740.058
Variance1016.6767
MonotonicityNot monotonic
2023-12-08T00:02:18.183422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.630000114 12
 
1.6%
1.36 12
 
1.6%
1.57 12
 
1.6%
1.21 12
 
1.6%
1.32 12
 
1.6%
1.33 12
 
1.6%
1.27 12
 
1.6%
1.42 12
 
1.6%
1.45 12
 
1.6%
1.5 12
 
1.6%
Other values (518) 636
84.1%
ValueCountFrequency (%)
1.21 12
1.6%
1.27 12
1.6%
1.32 12
1.6%
1.33 12
1.6%
1.36 12
1.6%
1.42 12
1.6%
1.45 12
1.6%
1.5 12
1.6%
1.52 12
1.6%
1.57 12
1.6%
ValueCountFrequency (%)
133.8730435 1
0.1%
133.0485714 1
0.1%
124.9286364 1
0.1%
123.9361905 1
0.1%
123.07 1
0.1%
120.4635 1
0.1%
120.08 1
0.1%
119.702381 1
0.1%
116.5195 1
0.1%
116.46 1
0.1%

oil_dubai
Real number (ℝ)

Distinct524
Distinct (%)69.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.23813
Minimum1.21
Maximum131.22478
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2023-12-08T00:02:18.519215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.21
5-th percentile1.33
Q110.4525
median18.55
Q345.576023
95-th percentile104.96917
Maximum131.22478
Range130.01478
Interquartile range (IQR)35.123523

Descriptive statistics

Standard deviation30.936611
Coefficient of variation (CV)0.99034774
Kurtosis0.62002136
Mean31.23813
Median Absolute Deviation (MAD)16.68
Skewness1.2311269
Sum23616.026
Variance957.07392
MonotonicityNot monotonic
2023-12-08T00:02:18.866002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.630000114 12
 
1.6%
1.57 12
 
1.6%
1.52 12
 
1.6%
1.5 12
 
1.6%
1.45 12
 
1.6%
1.42 12
 
1.6%
1.36 12
 
1.6%
1.33 12
 
1.6%
1.32 12
 
1.6%
1.27 12
 
1.6%
Other values (514) 636
84.1%
ValueCountFrequency (%)
1.21 12
1.6%
1.27 12
1.6%
1.32 12
1.6%
1.33 12
1.6%
1.36 12
1.6%
1.42 12
1.6%
1.45 12
1.6%
1.5 12
1.6%
1.52 12
1.6%
1.57 12
1.6%
ValueCountFrequency (%)
131.2247826 1
0.1%
127.587619 1
0.1%
122.2759091 1
0.1%
118.9486364 1
0.1%
117.25 1
0.1%
116.1461905 1
0.1%
115.73 1
0.1%
115.7 1
0.1%
113.2109524 1
0.1%
113.11 1
0.1%

coffee_arabica
Real number (ℝ)

Distinct733
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5765548
Minimum0.7776
Maximum7.0036
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2023-12-08T00:02:19.286743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.7776
5-th percentile0.854325
Q11.351625
median2.6977944
Q33.31295
95-th percentile4.97505
Maximum7.0036
Range6.226
Interquartile range (IQR)1.961325

Descriptive statistics

Standard deviation1.3424536
Coefficient of variation (CV)0.52102659
Kurtosis-0.10152662
Mean2.5765548
Median Absolute Deviation (MAD)1.03925
Skewness0.59039769
Sum1947.8754
Variance1.8021816
MonotonicityNot monotonic
2023-12-08T00:02:19.599554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8891 3
 
0.4%
2.9427 2
 
0.3%
0.8929 2
 
0.3%
2.976898386 2
 
0.3%
0.8267 2
 
0.3%
3.10630958 2
 
0.3%
0.9026 2
 
0.3%
0.8109 2
 
0.3%
0.8047 2
 
0.3%
0.8256 2
 
0.3%
Other values (723) 735
97.2%
ValueCountFrequency (%)
0.7776 1
0.1%
0.795 1
0.1%
0.797 1
0.1%
0.7992 1
0.1%
0.7998 1
0.1%
0.802 1
0.1%
0.8031 1
0.1%
0.8042 1
0.1%
0.8047 2
0.3%
0.8064 1
0.1%
ValueCountFrequency (%)
7.0036 1
0.1%
6.7058 1
0.1%
6.616505544 1
0.1%
6.439033634 1
0.1%
6.417428358 1
0.1%
6.346880518 1
0.1%
6.2889 1
0.1%
6.169188146 1
0.1%
6.062264076 1
0.1%
6.060059456 1
0.1%

coffee_robustas
Real number (ℝ)

Distinct715
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7274784
Minimum0.4872098
Maximum6.883547
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2023-12-08T00:02:19.930349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.4872098
5-th percentile0.66390605
Q10.92305308
median1.6321718
Q32.2822
95-th percentile3.4715457
Maximum6.883547
Range6.3963372
Interquartile range (IQR)1.3591469

Descriptive statistics

Standard deviation0.94074785
Coefficient of variation (CV)0.54457866
Kurtosis2.5466008
Mean1.7274784
Median Absolute Deviation (MAD)0.69015937
Skewness1.1896824
Sum1305.9736
Variance0.88500652
MonotonicityNot monotonic
2023-12-08T00:02:20.497007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6684254 5
 
0.7%
0.6723936 4
 
0.5%
0.6968643 3
 
0.4%
0.6765823 3
 
0.4%
0.9298873 3
 
0.4%
0.6117679 3
 
0.4%
0.6157362 3
 
0.4%
0.9400282 3
 
0.4%
2.041257658 2
 
0.3%
0.6887074 2
 
0.3%
Other values (705) 725
95.9%
ValueCountFrequency (%)
0.4872098 1
0.1%
0.5029 1
0.1%
0.5124 1
0.1%
0.5221 1
0.1%
0.5351 1
0.1%
0.5368 1
0.1%
0.5373 1
0.1%
0.5469536 1
0.1%
0.5692 1
0.1%
0.5798017 1
0.1%
ValueCountFrequency (%)
6.883547 1
0.1%
6.747966 1
0.1%
5.942636 1
0.1%
5.431397 1
0.1%
4.938676 1
0.1%
4.767601 1
0.1%
4.496439 1
0.1%
4.482551 1
0.1%
4.442207 1
0.1%
4.331758 1
0.1%

tea_columbo
Real number (ℝ)

Distinct608
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7779616
Minimum0.4341979
Maximum4.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2023-12-08T00:02:21.084641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.4341979
5-th percentile0.635353
Q10.8925009
median1.5040005
Q32.5152043
95-th percentile3.6025
Maximum4.49
Range4.0558021
Interquartile range (IQR)1.6227034

Descriptive statistics

Standard deviation1.0086791
Coefficient of variation (CV)0.56732334
Kurtosis-0.66875673
Mean1.7779616
Median Absolute Deviation (MAD)0.6576996
Skewness0.74423496
Sum1344.139
Variance1.0174335
MonotonicityNot monotonic
2023-12-08T00:02:21.501382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9303009 12
 
1.6%
0.8610009 12
 
1.6%
0.8148008 12
 
1.6%
0.8190008 12
 
1.6%
0.8463009 12
 
1.6%
0.7602008 12
 
1.6%
0.6770413 12
 
1.6%
0.5964011 12
 
1.6%
0.6266412 12
 
1.6%
0.8925009 12
 
1.6%
Other values (598) 636
84.1%
ValueCountFrequency (%)
0.4341979 1
 
0.1%
0.5221966 1
 
0.1%
0.5455723 1
 
0.1%
0.5837871 1
 
0.1%
0.5846411 1
 
0.1%
0.5964011 12
1.6%
0.6023916 1
 
0.1%
0.6031651 1
 
0.1%
0.6047198 1
 
0.1%
0.617284 1
 
0.1%
ValueCountFrequency (%)
4.49 1
0.1%
4.27 1
0.1%
4.21 1
0.1%
4.19 1
0.1%
4.16 1
0.1%
4.135620511 1
0.1%
4.13 1
0.1%
4.11 2
0.3%
4.1 1
0.1%
4.09 1
0.1%

tea_kolkata
Real number (ℝ)

Distinct634
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8703082
Minimum0.6647995
Maximum4.0730112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2023-12-08T00:02:22.105011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.6647995
5-th percentile0.7986667
Q11.2973687
median1.8506122
Q32.3768987
95-th percentile3.0886475
Maximum4.0730112
Range3.4082117
Interquartile range (IQR)1.07953

Descriptive statistics

Standard deviation0.69786695
Coefficient of variation (CV)0.3731294
Kurtosis-0.55790982
Mean1.8703082
Median Absolute Deviation (MAD)0.53728308
Skewness0.26941242
Sum1413.953
Variance0.48701829
MonotonicityNot monotonic
2023-12-08T00:02:23.083409image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.121401 12
 
1.6%
0.7986667 12
 
1.6%
0.978601 12
 
1.6%
0.9906667 12
 
1.6%
0.7333334 12
 
1.6%
0.8626667 12
 
1.6%
1.142401 12
 
1.6%
1.039501 12
 
1.6%
1.083601 12
 
1.6%
1.104601 12
 
1.6%
Other values (624) 636
84.1%
ValueCountFrequency (%)
0.6647995 1
 
0.1%
0.6855978 1
 
0.1%
0.7084 1
 
0.1%
0.7098448 1
 
0.1%
0.7105354 1
 
0.1%
0.7253865 1
 
0.1%
0.7323374 1
 
0.1%
0.7333334 12
1.6%
0.7353197 1
 
0.1%
0.7530807 1
 
0.1%
ValueCountFrequency (%)
4.073011154 1
0.1%
3.990422346 1
0.1%
3.957038 1
0.1%
3.856839612 1
0.1%
3.550720905 1
0.1%
3.538153514 1
0.1%
3.504758045 1
0.1%
3.469254 1
0.1%
3.412294528 1
0.1%
3.351542111 1
0.1%

tea_mombasa
Real number (ℝ)

Distinct531
Distinct (%)70.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6712216
Minimum0.7195997
Maximum3.3925
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2023-12-08T00:02:23.644063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.7195997
5-th percentile0.8357997
Q11.1368
median1.5982575
Q32.0838297
95-th percentile2.874375
Maximum3.3925
Range2.6729003
Interquartile range (IQR)0.94702967

Descriptive statistics

Standard deviation0.61535745
Coefficient of variation (CV)0.36820818
Kurtosis-0.47147177
Mean1.6712216
Median Absolute Deviation (MAD)0.4614575
Skewness0.53555512
Sum1263.4435
Variance0.37866479
MonotonicityNot monotonic
2023-12-08T00:02:24.452567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0374 12
 
1.6%
0.8357997 12
 
1.6%
0.9841996 12
 
1.6%
1.1368 12
 
1.6%
0.8105997 12
 
1.6%
0.8875996 12
 
1.6%
0.7195997 12
 
1.6%
0.9015996 12
 
1.6%
0.9911996 12
 
1.6%
0.9141996 12
 
1.6%
Other values (521) 636
84.1%
ValueCountFrequency (%)
0.7195997 12
1.6%
0.8105997 12
1.6%
0.8203997 5
0.7%
0.8357997 12
1.6%
0.8492754 7
0.9%
0.8875996 12
1.6%
0.9015996 12
1.6%
0.9141996 12
1.6%
0.9225996 12
1.6%
0.9533996 12
1.6%
ValueCountFrequency (%)
3.3925 1
0.1%
3.268 1
0.1%
3.24 1
0.1%
3.17 1
0.1%
3.1375 1
0.1%
3.1025 1
0.1%
3.0925 1
0.1%
3.0825 1
0.1%
3.077708 1
0.1%
3.076666667 1
0.1%

sugar_eu
Real number (ℝ)

Distinct592
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4051577
Minimum0.11221516
Maximum0.78317064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2023-12-08T00:02:25.856709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.11221516
5-th percentile0.11221516
Q10.29811974
median0.40234315
Q30.56951938
95-th percentile0.6808
Maximum0.78317064
Range0.67095548
Interquartile range (IQR)0.27139964

Descriptive statistics

Standard deviation0.18774134
Coefficient of variation (CV)0.46337844
Kurtosis-1.0493253
Mean0.4051577
Median Absolute Deviation (MAD)0.14906998
Skewness-0.16765975
Sum306.29922
Variance0.035246812
MonotonicityNot monotonic
2023-12-08T00:02:26.860085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.112215158 43
 
5.7%
0.12676565 23
 
3.0%
0.12456103 13
 
1.7%
0.12235641 12
 
1.6%
0.129411194 12
 
1.6%
0.124340568 12
 
1.6%
0.130954428 12
 
1.6%
0.126324726 12
 
1.6%
0.295639542 4
 
0.5%
0.40234315 3
 
0.4%
Other values (582) 610
80.7%
ValueCountFrequency (%)
0.112215158 43
5.7%
0.113758392 1
 
0.1%
0.115522088 1
 
0.1%
0.116403936 1
 
0.1%
0.116624398 1
 
0.1%
0.118167632 1
 
0.1%
0.12235641 12
 
1.6%
0.124340568 12
 
1.6%
0.12456103 13
 
1.7%
0.126324726 12
 
1.6%
ValueCountFrequency (%)
0.78317064 1
0.1%
0.7822045029 1
0.1%
0.77287176 1
0.1%
0.7726612114 1
0.1%
0.7714 1
0.1%
0.7439769257 1
0.1%
0.7326995657 1
0.1%
0.7311857236 1
0.1%
0.7293000343 1
0.1%
0.7239194259 1
0.1%

sugar_us
Real number (ℝ)

Distinct595
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.43246218
Minimum0.11684486
Maximum1.2632473
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2023-12-08T00:02:28.024372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.11684486
5-th percentile0.13007258
Q10.2976237
median0.4711192
Q30.51218834
95-th percentile0.78043548
Maximum1.2632473
Range1.1464024
Interquartile range (IQR)0.21456464

Descriptive statistics

Standard deviation0.18858876
Coefficient of variation (CV)0.43608151
Kurtosis0.11824936
Mean0.43246218
Median Absolute Deviation (MAD)0.081319197
Skewness0.10340297
Sum326.94141
Variance0.035565721
MonotonicityNot monotonic
2023-12-08T00:02:28.734930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.12786796 14
 
1.9%
0.14991416 9
 
1.2%
0.12345872 8
 
1.1%
0.13007258 8
 
1.1%
0.13889106 8
 
1.1%
0.1543234 7
 
0.9%
0.15873264 7
 
0.9%
0.1322772 7
 
0.9%
0.13668644 7
 
0.9%
0.14770954 7
 
0.9%
Other values (585) 674
89.2%
ValueCountFrequency (%)
0.11684486 1
 
0.1%
0.11904948 1
 
0.1%
0.1212541 4
 
0.5%
0.12345872 8
1.1%
0.12566334 4
 
0.5%
0.12786796 14
1.9%
0.13007258 8
1.1%
0.1322772 7
0.9%
0.13448182 3
 
0.4%
0.13668644 7
0.9%
ValueCountFrequency (%)
1.26324726 1
0.1%
1.028234768 1
0.1%
0.91932654 1
0.1%
0.887405963 1
0.1%
0.8852599119 1
0.1%
0.88515493 1
0.1%
0.8802568395 1
0.1%
0.8750903604 1
0.1%
0.873911368 1
0.1%
0.867692019 1
0.1%

sugar_world
Real number (ℝ)

Distinct694
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24026341
Minimum0.0287
Maximum1.2377
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2023-12-08T00:02:29.164666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.0287
5-th percentile0.048275
Q10.13970468
median0.21528546
Q30.309325
95-th percentile0.51980635
Maximum1.2377
Range1.209
Interquartile range (IQR)0.16962032

Descriptive statistics

Standard deviation0.15194696
Coefficient of variation (CV)0.63241824
Kurtosis4.3226874
Mean0.24026341
Median Absolute Deviation (MAD)0.083338956
Skewness1.4836942
Sum181.63914
Variance0.02308788
MonotonicityNot monotonic
2023-12-08T00:02:29.522443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.037 4
 
0.5%
0.0666 3
 
0.4%
0.2641 3
 
0.4%
0.0805 3
 
0.4%
0.0626 3
 
0.4%
0.0337 2
 
0.3%
0.1788 2
 
0.3%
0.0452 2
 
0.3%
0.189376858 2
 
0.3%
0.1726 2
 
0.3%
Other values (684) 730
96.6%
ValueCountFrequency (%)
0.0287 1
 
0.1%
0.0298 1
 
0.1%
0.0304 1
 
0.1%
0.0311 1
 
0.1%
0.0329 1
 
0.1%
0.0337 2
0.3%
0.0346 1
 
0.1%
0.0359 2
0.3%
0.0362 1
 
0.1%
0.037 4
0.5%
ValueCountFrequency (%)
1.2377 1
0.1%
0.9894 1
0.1%
0.894 1
0.1%
0.8708 1
0.1%
0.8445 1
0.1%
0.833 1
0.1%
0.7649 1
0.1%
0.7529 1
0.1%
0.7491 1
0.1%
0.7028 1
0.1%

Month
Real number (ℝ)

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2023-12-08T00:02:29.802270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median6.5
Q39.25
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.4543379
Coefficient of variation (CV)0.5314366
Kurtosis-1.2168905
Mean6.5
Median Absolute Deviation (MAD)3
Skewness0
Sum4914
Variance11.93245
MonotonicityNot monotonic
2023-12-08T00:02:30.068107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 63
8.3%
2 63
8.3%
3 63
8.3%
4 63
8.3%
5 63
8.3%
6 63
8.3%
7 63
8.3%
8 63
8.3%
9 63
8.3%
10 63
8.3%
Other values (2) 126
16.7%
ValueCountFrequency (%)
1 63
8.3%
2 63
8.3%
3 63
8.3%
4 63
8.3%
5 63
8.3%
6 63
8.3%
7 63
8.3%
8 63
8.3%
9 63
8.3%
10 63
8.3%
ValueCountFrequency (%)
12 63
8.3%
11 63
8.3%
10 63
8.3%
9 63
8.3%
8 63
8.3%
7 63
8.3%
6 63
8.3%
5 63
8.3%
4 63
8.3%
3 63
8.3%

Year
Real number (ℝ)

Distinct63
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1991
Minimum1960
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2023-12-08T00:02:30.366923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1960
5-th percentile1963
Q11975
median1991
Q32007
95-th percentile2019
Maximum2022
Range62
Interquartile range (IQR)32

Descriptive statistics

Standard deviation18.196281
Coefficient of variation (CV)0.0091392671
Kurtosis-1.2006046
Mean1991
Median Absolute Deviation (MAD)16
Skewness0
Sum1505196
Variance331.10464
MonotonicityIncreasing
2023-12-08T00:02:30.737694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1960 12
 
1.6%
2007 12
 
1.6%
1994 12
 
1.6%
1995 12
 
1.6%
1996 12
 
1.6%
1997 12
 
1.6%
1998 12
 
1.6%
1999 12
 
1.6%
2000 12
 
1.6%
2001 12
 
1.6%
Other values (53) 636
84.1%
ValueCountFrequency (%)
1960 12
1.6%
1961 12
1.6%
1962 12
1.6%
1963 12
1.6%
1964 12
1.6%
1965 12
1.6%
1966 12
1.6%
1967 12
1.6%
1968 12
1.6%
1969 12
1.6%
ValueCountFrequency (%)
2022 12
1.6%
2021 12
1.6%
2020 12
1.6%
2019 12
1.6%
2018 12
1.6%
2017 12
1.6%
2016 12
1.6%
2015 12
1.6%
2014 12
1.6%
2013 12
1.6%

Interactions

2023-12-08T00:02:12.594471image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:20.875823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:25.411188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:29.652579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:33.893969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:38.277271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:42.555959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:47.099166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:51.840249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:56.211555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:02.099931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:08.018288image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:12.952249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:21.407655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:25.746982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:29.965384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:34.244754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:38.602073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:42.861775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:47.523902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:52.174040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:56.657279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:02.861462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:08.510986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:13.249069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:21.832391image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:26.078779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:30.467077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:34.668490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:38.998824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:43.157593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:47.943645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:52.629760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:57.024056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:03.401129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:08.910745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:13.568870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:22.196171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:26.600458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:30.810864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:35.097229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:39.448550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:43.504378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:48.273440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:53.008528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:57.373841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:03.955787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:09.357462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:13.935643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:22.555948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:26.919265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:31.170646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:35.462005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:39.789337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:43.828180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:48.685191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:53.359315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:57.704637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:04.375529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:09.700253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:14.335398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:22.952702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:27.272043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:31.502439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:35.882745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:40.175102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:44.174966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:49.056960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:53.753069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:58.124377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:04.761295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:09.994072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:14.695181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:23.462390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:27.668800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:31.847229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:36.226533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:40.481912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:44.555733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:49.396751image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:54.111848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:58.471165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:05.333941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:10.407818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:15.030969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:23.771205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:27.955623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:32.157036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:36.508359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:40.776731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:45.008450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:49.671583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:54.432648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:58.771979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:05.610771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:10.973469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:15.329787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:24.136977image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:28.274427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:32.568781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:36.807176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:41.187480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:45.458174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:50.034359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:54.813416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:59.307652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:05.961552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:11.406201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:15.669580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:24.522738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:28.595229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:32.900577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:37.094996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:41.517598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:45.862926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:50.577023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:55.172195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:59.849316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:06.315339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:11.734999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:16.012368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:24.822551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:29.030961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:33.271350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:37.490755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:41.813416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:46.229697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:51.004761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:55.496998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:00.524907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:06.699098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:12.030817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:16.289197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:25.094385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:29.358757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:33.543183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:37.827546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:42.226162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:46.696414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:51.409510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:01:55.887754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:01.111541image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:07.313719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-12-08T00:02:12.322640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-12-08T00:02:31.014540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
oil_brentoil_dubaicoffee_arabicacoffee_robustastea_columbotea_kolkatatea_mombasasugar_eusugar_ussugar_worldMonthYear
oil_brent1.0000.9980.6850.4820.8460.7350.8350.5290.7100.6890.0170.869
oil_dubai0.9981.0000.6930.4940.8460.7350.8390.5200.7120.6890.0200.868
coffee_arabica0.6850.6931.0000.8600.6410.7320.7740.3470.6420.616-0.0050.632
coffee_robustas0.4820.4940.8601.0000.3680.5990.5780.2490.4590.4620.0090.339
tea_columbo0.8460.8460.6410.3681.0000.7580.8550.4910.7520.6120.0010.899
tea_kolkata0.7350.7350.7320.5990.7581.0000.7880.4150.7110.5660.1080.682
tea_mombasa0.8350.8390.7740.5780.8550.7881.0000.4010.6940.601-0.0230.795
sugar_eu0.5290.5200.3470.2490.4910.4150.4011.0000.4850.3790.0010.571
sugar_us0.7100.7120.6420.4590.7520.7110.6940.4851.0000.7770.0130.781
sugar_world0.6890.6890.6160.4620.6120.5660.6010.3790.7771.000-0.0120.644
Month0.0170.020-0.0050.0090.0010.108-0.0230.0010.013-0.0121.0000.000
Year0.8690.8680.6320.3390.8990.6820.7950.5710.7810.6440.0001.000

Missing values

2023-12-08T00:02:16.793885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-08T00:02:17.299572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

oil_brentoil_dubaicoffee_arabicacoffee_robustastea_columbotea_kolkatatea_mombasasugar_eusugar_ussugar_worldMonthYear
date
1960-01-011.631.630.94090.6968640.9303011.1214011.03740.1223560.1168450.066611960
1960-02-011.631.630.94690.6887070.9303011.1214011.03740.1223560.1190490.067921960
1960-03-011.631.630.92810.6887070.9303011.1214011.03740.1223560.1212540.068331960
1960-04-011.631.630.93030.6845190.9303011.1214011.03740.1223560.1234590.068141960
1960-05-011.631.630.92000.6906920.9303011.1214011.03740.1223560.1212540.068351960
1960-06-011.631.630.91230.6968640.9303011.1214011.03740.1223560.1256630.066661960
1960-07-011.631.630.91600.6906920.9303011.1214011.03740.1223560.1322770.072871960
1960-08-011.631.630.92920.6988480.9303011.1214011.03740.1223560.1278680.074181960
1960-09-011.631.630.92260.7028170.9303011.1214011.03740.1223560.1322770.072591960
1960-10-011.631.630.92370.7067850.9303011.1214011.03740.1223560.1300730.0538101960
oil_brentoil_dubaicoffee_arabicacoffee_robustastea_columbotea_kolkatatea_mombasasugar_eusugar_ussugar_worldMonthYear
date
2022-03-01115.59113.115.6985022.2888363.3500001.9559462.5350000.3598360.8009380.41998032022
2022-04-01105.78102.685.8541482.2910414.0900003.1070042.5250000.3534690.8135050.43342842022
2022-05-01112.37108.325.7412712.2729633.7100002.8690832.3766670.3454680.8020410.42879952022
2022-06-01120.08115.736.0338242.2886163.5100003.2728322.1100000.3453050.7930020.41777562022
2022-07-01108.92106.485.6391972.2121164.0100003.5507212.3650000.3326010.7678690.40278472022
2022-08-0198.6097.755.9178612.4173664.2100003.5381542.3600000.3307730.7821990.39352582022
2022-09-0190.1690.635.8971382.4550654.4900003.1531982.3600000.3236210.7709560.39065992022
2022-10-0193.1390.595.2928522.2709794.1356212.8331122.4575000.3209430.7625780.386911102022
2022-11-0191.0786.284.7154622.0412583.8315282.8499792.4900000.3329930.7923400.407414112022
2022-12-0180.9076.784.6294822.0454463.9940732.4215162.3866670.3457290.8051270.417335122022